Visualize the NC elections results

This week's National Council elections results were reported in this form or the other by the media and by the Election Committee of Bhutan. Most of the results were reported one dzongkhag at a time, which made it difficult to see the overall picture. Using the numbers from the ECB's report (which were presented in one big table), I created two dashboards, each telling a story. One about voter percentage and the other about winner share of votes.

Dashboard 1: Voter Participation by Dzongkhag

I computed the per-dzongkhag percentage of votes (= total votes for all candidates / registered voters). Some dzongkhags clearly stand out! To focus on a particular dzongkhag, click on the map, bar-chart or scatter plot.

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Dashboard 2: Winner's Share of Votes

The map shows the % of votes that the winner received from the total number of votes in his dzongkhag. Some winners received a very large majority, while others did not. We'd expect that the share would be affected by the number of candidates: the more candidates, the lower the winner's share. While this is the case in most dzongkhags, notice an interesting exception: Samtse's Sangay Khandu won almost 49% of the votes despite having the largest number of competing candidates. Also, in Dagana and Trashigang, which had a single contestant, each received more than 90% ("yes") votes, which is much higher than even dzongkhags with two contestants.

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These dashboards were created with the free Tableau Public tool. It would be great to see the media and other organizations create similar visualizations and interactive dashboards for the upcoming elections.

Resist the temptation: avoid pie charts

With the upcoming elections, numbers can reveal important information. Unfortunately, our human brains are not that great at translation lots of numbers into a coherent stories. That's why we need charts and table to summarize and preset the data for us in meaningful ways.

Yesterday's Kuensel article "Youth to the fore in Thimphu" told the story of the young candidates in Thimphu. The main visual in the article was a pie chart showing the number of eligible voters in Thimphu, broken down by gewog. The reason for presenting these numbers was to relate the chances of election to the number of registered eligible voters in that gewog ("...if the number of voters in gewogs is of any assurance [for getting elected]"). I was therefore curious to figure out the story that the chart was trying to tell. Here is what I saw:

Chart from Kuensel, April 13, 2013

The first questions that come to mind are: which are the gewogs with the highest numbers of eligible voters? The lowest? The difference between the highest and lowest? etc. We explained in many previous posts that pie charts are notoriously ineffective for conveying information and answering basic questions such as those above. 3D charts are even worse. Pie charts not only make it difficult to decipher information, but they can also lead to misinformation.

I once again diligently created a bar chart version to help us all better see the data. While doing this, I started wondering about the meaning of these numbers. How do they relate to the population size of the gewog? I went to NSB's website and grabbed 2011 gewog-wise population statistics for Thimphu. I then compared the eligible voter numbers to the population numbers. I also computed the eligible voters as a percentage of that gewog's population. Take a look at the dashboard below. Not surprisingly, Thimphu Thromde has the largest number of eligible voters (those eligible to vote in Thimphu). But, relative to its population, it has the lowest % of eligible voters (8%). Maybe this is not surprising, because so many Thromde residents are not "native". The next two gewogs with highest eligible voter numbers are Mewang and Karwang. If we compare to population numbers, the third largest gewog after Thromde and Mewang is... Chang! (sort by the red bar chart or table below it). This means that Chang, although 3rd in terms of population size in the dzongkhag, has a relatively low number of eligible voters. Finally, sorting by % of eligible voters (green bars), we see the actual percentages in each gewog, with Naro leading (91% eligible voters) and then numbers declining ten-fold when we get to Thromde (8%) .

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Whether the actual voter numbers tell a more important story than their percentage of the gewog population, our main point is to highlight the power of bar charts (and especially when sorted by bar length), and the inappropriateness of pie charts, not to mention 3D pie charts. Once again, we subscribe to visualization expert Stephen Few's motto "Save the Pies for Dessert".

Visually beautiful, but scientifically ineffective

Congratulations to the new Raven magazine that was launched this month. The first issue is beautifully designed. Kudos also for reporting on a scientific survey about Pedestrian Day opinions. The reporter clearly explained issues with the previous surveys (which we wrote about in an earlier post). There is also an attempt to use charts for telling the story, and I am a big fan of charts as storytellers. However, creating effective charts requires more than Excel skills.

With all this good work, the last mile is to replace the current ineffective and confusing charts with effective ones. Below are some charts from the article on Pedestrian Day. Start from this chart. What does it teach us?

The pie chart is trying to convey the "percentage of respondents wanting Ped Day to be discontinued". A main feature of a pie chart is that its parts should add up to 100%. Yet, the percentages on the slices here do not add up to 100%, causing confusion. A careful look uncovers that the person creating the chart was trying to convey the percentage separately for each sector. If that's the case, then the pie chart is not set up correctly! You would need separate pies for separate sectors (civil servants, H-wives,...), and then each pie would have a breakdown of  that sector's opinion of Ped Day. Using a pie chart incorrectly is very misleading.

But even using a pie chart correctly is known to be ineffective at best, and misleading at worse. Our brain is not good at comparing sizes of pizza slices. With 3D pizza slices, we are even worse off! The best solution: use a bar chart.

Here's a simple bar chart of the same data, using Excel. See if you get a better idea of responses to this survey question by different sectors (by the way, might a truck driver also be "Business" or "Pvt Employee"?)



While we're on the subject of 3D charts, you can ruin a perfectly effective bar chart by turning it into a 3D bar chart, as you can see in the following charts (from the same article). I included the original 3D chart alongside an ordinary bar chart of the same data, which I created in Excel. Not only is there no need for the third dimension (it does not represent any information in these cases), but it makes the comparison between bars much more difficult on our brain.

Left: original 3D chart. Right: ordinary bar chart for the same data

Left: original 3D chart. Right: ordinary bar chart for the same data

Creating effective charts is not rocket science and does not require expensive software. It does require learning key principles and implementing them meticulously. The media should invest in training editors and reporters in effective data presentation -- not only creating effective charts, but also being able to critically assess charts presented by their sources.

Weather forecasts in the media: hot or cold?

Several local newspapers have weather forecasts in their online and print versions. While forecasts from different sources can differ (after all, these are uncertain forecasts), I found a few perplexing issues when examining forecasts from the two oldest papers: Kuensel and Bhutan Observer.

Kuensel takes a safety measure of giving high/low temperature and outlook forecasts only for "today". The first perplexing fact is the discrepancy between the print and online forecasts. Here's an example from today:

Kuensel's weather forecast. Left: print edition. Right: Kuenselonline.com (accessed 27 April, 2012, 4pm)

Turns out that the Kuensel website gets updated in the evening, so in fact their website does "back-casting" of yesterday's weather... The next strange discrepancy is between the names of the Dzongkhags as they appear in print and online (not to mention the different ordering). Third, I couldn't find the source for the forecasts. Where are they coming from? Finally, it would be helpful if the graphics in both modes used the same icons, order of columns, column names, and design.

Bhutan Observer has a Weekly Weather corner in its print and online editions. It offers more detail than Kuensel in terms of listing all the 20 Dzongkhags and provides forecasts for an entire week. However, unlike Kuensel which also reports high/low temperature forecasts, BO's graphic table tells us only the "outlook", that is, whether a day is likely to be of one of six types: mostly sunny skies, possibility of rain, cloudy, partly cloudy, partly sunny, or partly cloudy chances of snow/sleet. This choice of information is a bit unusual. I can see how it is useful for determining whether an umbrella, hat or boots are needed. It can also help guess the chances of flights landing in Paro.

And now to the data presentation: Is it easy to quickly figure out the weather information that a reader would be interested in? Here are some questions that a reader might ask:

Weekly corner from Bhutan Observer's website

1. What is the forecast for Tuesday in Chukha?
2. How many different weather outlooks across Bhutan in the coming week?
3. How many "partly sunny" days anywhere in Bhutan? (good for trekking?)

In trying to answer these questions you probably noticed a few challenges:

• Dzongkhag names are not listed alphabetically! With 20 names, searching for Chukha takes quite some time. 
• Distinguishing between the icon images is difficult because they seem similar. A simple trick that would have made it much easier on our eye is using different colours. On the website there is no reason to use greyscale. And if colour-printing is too expensive, then using a more distinctive greyscale can work.
• Given that there are only two different icons in this week's table, do we really need to see all six possibilities in the bottom legend? Removing unused icons from the legend would make the reader's task much easier.

Lastly, one should always ask about the data source. Here the source is listed clearly at the top as "Contributed by the Metrological Department of Bhutan". I suppose this is the Hydro-Meterological Services Division at the Department of Energy?

Reporting statistics in the media

Today's front page Kuensel article, Where bullocks beat power tillers, described how in most Dzongkhags bullocks are still the main farming "technology". The article included a table that compared the percentage of households using bullocks, power tillers, bullocks and power tillers, and manual digging, broken down by Dzongkhag. Here is the table:

From Kuensel Newspaper (22 Nov, 2010)

While the table gives the exact percentages, it does not give the big picture, which was well described in the text. In such cases, as they say, "a picture is worth a thousand words". I have reproduced the statistics in a set of Dzongkhag-level bar charts. Each bar color (grey, blue, green, yellow) corresponds to a farming "technology" (bullocks, power tillers, bullocks and power tillers, manual digging). You can now directly compare the 20 Dzongkhags (the scale is identical in all charts).

From this visualization it is easy to see that indeed, most Dzongkhags are dominated by the use of bullocks only. Bumthang, Gasa, Paro, Punakha,Thimphu, and Wangdue are the heaviest users of power tillers. And what about manual digging?

% of households using each of 4 farming technologies, by Dzongkhag

While the chart might not convey the exact percentages, it allows readers to quickly see the large picture and even the approximate percentages.

Streaming audio and/or video for free

Today we met Ron, who is trying to help a local radio station stream their programming via the Internet to listeners worldwide. I mentioned to him a great free solution that I used in 2008 to stream seminar talks -- www.ustream.tv. This is a free service that "enables anyone with a camera and an Internet connection to quickly and easily broadcast to an online global audience". It means that you can stream audio and video, and then even keep an archive hosted on their website.

Ustream.tv has a few big advantages:

• No special software needed to broadcast or to view (beyond Flash)
• Easy-to-use interface
• It's been running since 2007 - this is a good record for such a service
• "Users can broadcast events to an audience of unlimited size, and enable interaction through chat and Social Stream" (from their website)
• A player can be embedded directly into your own website
• Free! This is key in Bhutan, not so much because it saves money but because it means that a credit card is unnecessary (given that there are no credit cards here)

You might ask what is Ustream's business model if this is all free. The answer is advertising, and as in many other "freemium" services, they also offer a more elaborate paid version called Watershed.

Ustream.tv is a really cool option not only for small radio stations but also for journalists on the go, who have a laptop with mobile Internet connection, a microphone and a webcam. No need for a camera crew in order to report live!